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Residential customer needs Appendix 6 – Conjoint analysis
1 of 21
Appendix 6 – Conjoint analysis
This appendix provides a short explanation of conjoint analysis and further
details about the propositions tested and the way the results have been
analysed
Conjoint analysis
Conjoint analysis is a statistical technique that helps us better understand
what people really value in products and services and how they make these
decisions. Every customer making choices between products and services is
faced with trade-offs. Is high quality more important than a low price and quick
delivery for instance? Customers find it difficult to answer these questions
directly and rationalise their choices, partly because we are asking them to
think about their preferences in a way that is unfamiliar to them, and partly
because other factors such as the desire to appear logical or socially
responsible constrain their responses.
Conjoint enables us to obtain the information on what choices people make
and what is driving their behaviour in a simpler and more reliable way. In a
conjoint exercise respondents are asked to choose between different product
concepts - descriptions of the full product or service with different
combinations of the component features. The task is therefore much more
straightforward for the respondent than asking them to rationalise the choices
they make themselves, as we simply ask what they would choose.
Conjoint analysis is used to analyse the choices people made and to help us
understand which features are driving those choices. The analysis provides
us with scores that not only summarise the influence different features have,
but that can also be used to model the appeal or acceptability of any
combination of the features tested, not just those combinations which were
evaluated in the exercise. Conjoint therefore provides us with an identification
of what is really driving customer behaviour, rather than what customers say
is important to them, and also offers a means of evaluating many more
product combinations than we could reasonably ask a respondent to do
directly.
Residential customer needs Appendix 6 – Conjoint analysis
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Propositions tested
The following figure summarises the propositions tested. Each feature is
represented by an attribute that has between two and four levels which define
the range of different levels of service which may be available within that
feature. The levels include the current service level.
Figure 1 - Summary of propositions Attribute Levels
75% of first class post arrives within 1 day
85% of first class post arrives within 1 day
First class quality of service
93% of first class post arrives within 1 day*
Single Service Single service
95% of post arrives within two days
Cost = 35p
No facility to specify evening / Saturday
delivery
Ability to specify evening /
Saturday delivery of items
requiring a signature or too
large to fit through letterbox Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per
item
Single insured & next day guaranteed
delivery service (£5.05)
Insured/guaranteed items
Insured service (£2.95)
Next day guaranteed delivery (£1.95)
5 days a week (Monday to Friday)
5 days a week (Saturday and 4 weekdays)
Collection and delivery of mail
6 days a week (Monday to Saturday)
first class = 41p
second class = 32p
first class = 45p
second class = 35p
first class = 49p
second class = 38p
Cost of postage
first class = 55p
second class = 42p
*The quality of service for second class was always shown at 98% (the standard is 98.5%,
but was rounded down in the questioning)
Residential customer needs Appendix 6 – Conjoint analysis
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Methodology
Rather than ask about attributes independently, the conjoint requires
respondents to assess them as part of a package. This avoids the pitfalls of a
simple evaluation of individual attributes which can often result in everything
being important and little real discrimination between features.
The use of levels within features is also important in aiding more accurate
measurement. Without levels we would simply be asking about the
importance of each attribute as a concept, which may be very easy to answer
but is very difficult to answer accurately or in a way we can really use the
responses. For example, the impact that price has on people’s perceptions of
the service depends on what price range we are considering – if we were put
up the price by 100%, then it is reasonable to assume that this would matter a
lot more to customers than a 10% price rise. It is impossible, therefore, to
have a truly meaningful response to the importance of price without knowing
what the parameters are – so by defining levels within the features we have
clearly set these parameters.
The levels of the attributes are combined to form concepts, which describe
different potential descriptions of the universal service offer. These are then
presented in pairs to the respondents who are asked which service they
prefer.
The questions used do not represent specific questions we want to answer or
include particular concepts / offers of interest, but are structured to form a
balanced design. The concepts are designed so that not only is each level of
each feature shown a similar number of times, but so that the combinations of
levels across different features is also similarly well balanced. The aim is to
cover as many different combinations as possible, so that the resultant model
can examine any combination of features, not just those tested.
To gather as much detail as possible it is desirable to use many different
questions, covering as many different combinations as we can. However,
limited interviewing time and the need to make the exercise as respondent-
friendly as possible means we cannot ask everyone to respond to a complete
set of possible offers. The approach used is to have a number of different
questionnaire designs that use different sets of product offers. These are
balanced within each set, so everyone sees all the different features but are
Residential customer needs Appendix 6 – Conjoint analysis
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also balanced across each version so that overall we have covered all
combinations of features.
A total of 72 ‘packages’ were designed covering different combinations of the
six attributes and levels within them. Residential customers were shown two
packages at a time and asked to choose the one that best met their postal
needs. They also had the option to say that neither was acceptable1. Each
residential customer was shown six different pairs of packages, so that across
all 12 packages, residential customers would have been exposed to each of
the elements three to four times.
It should be noted that the quality of service attribute refers to first class post,
so is only shown when we are looking at a two tier service, as is the price
feature since this also refers to the pricing structure of the two tier approach.
When we present a single tier service, the quality of this service has been
fixed 95% arriving within two days and the price has been fixed at 35p. This
has been done so that the exercise can include a measure of the reactions
towards a single tier two day approach but also concentrate on evaluating the
current two tier structure in more detail. Only the two tier system will therefore
vary in terms of price and quality. The exercise has been structured so that
the two tier service remains the primary focus and this type of service appears
more frequently throughout the exercise.
When choosing between concepts there may be occasions when none of the
options presented to the respondent is very acceptable to them. While they
may have a preference for one over the other, the reality is that they would
not choose either of them
By including a ‘none of these’ option to indicate this within the conjoint data
collection we not only avoid spurious choices being made, when in reality
nothing would be chosen, but also enable to modelling of the point at which
items become sufficiently attractive to be chosen. We can there identify not
1 When choosing between packages there may be occasions when neither of the options is
acceptable to them. While they may have a preference for one over the other, the reality is
that they would not choose either of them. The inclusion of the “none” option avoids spurious
choices being made. It also enables the modelling of the level at which elements become
sufficiently attractive to be chosen. We can therefore identify not just which combinations of
features are more or less attractive but which are attractive enough to be acceptable choices
and those which are not
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just which combinations of features are more or less attractive but which are
attractive enough to be acceptable choices and which are not.
The analysis not only creates values for each of the features and levels
included, but also calculates a value for the “none of these” option. This
equates to a threshold which we can include when modelling the
attractiveness of different packages, which tells us whether any given
package is above or below this point at which it becomes an acceptable
option.
An example of one version of the conjoint questions follows.
Residential customer needs Appendix 6 – Conjoint analysis
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Version 1
V1Q1 Option A Option B Option C
Service 85% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Single service
95% of post arrives within 2 days
Cost of postage first class = 45p
second class = 35p Single service = 35p
Collection and delivery of mail 5 days a week (Monday to Friday) 6 days a week (Monday to Saturday)
Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday
delivery of items requiring a signature or too large to fit through letterbox
No facility to specify evening / Saturday
delivery
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
Additional services available Insured service (£2.95)
Next day guaranteed delivery (£1.95)
Single insured & next day guaranteed delivery
service (£5.05)
V1Q2 Option A Option B Option C
Service 75% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
85% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Cost of postage first class = 49p
second class = 38p
first class = 41p
second class = 32p
Collection and delivery of mail 5 days a week (Saturday and 4 weekdays) 6 days a week (Monday to Saturday)
Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday delivery of items requiring a signature or
too large to fit through letterbox
No facility to specify evening / Saturday
delivery
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
Additional services available Single insured & next day guaranteed delivery
service (£5.05)
Insured service (£2.95)
Next day guaranteed delivery (£1.95)
Residential customer needs Appendix 6 – Conjoint analysis
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V1Q3 Option A Option B Option C
Service 75% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
93% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Cost of postage first class = 55p
second class = 42p
first class = 41p
second class = 32p
Collection and delivery of mail 5 days a week (Saturday and 4 weekdays) 5 days a week (Monday to Friday)
Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday delivery of items requiring a signature or
too large to fit through letterbox
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
No facility to specify evening / Saturday
delivery
Additional services available Insured service (£2.95)
Next day guaranteed delivery (£1.95)
Single insured & next day guaranteed delivery
service (£5.05)
V1Q4 Option A Option B Option C
Service Single service
95% of post arrives within 2 days
75% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Cost of postage Single service = 35p first class = 45p
second class = 35p
Collection and delivery of mail 6 days a week (Monday to Saturday) 5 days a week (Monday to Friday) Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday
delivery of items requiring a signature or
too large to fit through letterbox
No facility to specify evening / Saturday
delivery
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
Additional services available Insured service (£2.95)
Next day guaranteed delivery (£1.95)
Single insured & next day guaranteed delivery
service (£5.05)
Residential customer needs Appendix 6 – Conjoint analysis
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V1Q5 Option A Option B Option C
Service 85% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
75% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Cost of postage first class = 49p
second class = 38p
first class = 45p
second class = 35p
Collection and delivery of mail 5 days a week (Saturday and 4 weekdays) 6 days a week (Monday to Saturday) Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday delivery of items requiring a signature or
too large to fit through letterbox
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
No facility to specify evening / Saturday
delivery
Additional services available Single insured & next day guaranteed delivery
service (£5.05)
Insured service (£2.95)
Next day guaranteed delivery (£1.95)
V1Q6 Option A Option B Option C
Service 93% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
85% of 1st class post arrives within 1 day
98% of 2nd class post arrives within 3 days
Cost of postage first class = 41p
second class = 32p
first class = 55p
second class = 42p
Collection and delivery of mail 5 days a week (Monday to Friday) 5 days a week (Saturday and 4 weekdays) Neither option is acceptable, I would have to
use an alternative method
Ability to specify evening / Saturday
delivery of items requiring a signature or
too large to fit through letterbox
Sender can specify an evening or Saturday
delivery for an additional fee of £1.00 per item
No facility to specify evening / Saturday
delivery
Additional services available Insured service (£2.95)
Next day guaranteed delivery (£1.95)
Single insured & next day guaranteed delivery
service (£5.05)
Residential customer needs Appendix 6 – Conjoint analysis
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The conjoint analysis
Using attractiveness and acceptability to identify customer needs from a
universal service
The most common use of conjoint is to measure the attractiveness of different
products or services, so that we can model which of them would be chosen.
The total attractiveness of a product is the degree to which people want it –
the more attractive it is, the more they will want it. The one they want the most
is the one they will choose, which in most cases means it is the one they will
buy.
Typically this is because the products/services are on offer in a competitive
environment and the best way to ensure that a product is purchased is to
make sure it is the most attractive option on offer. Customers can choose
whether to buy any product or not to buy at all, so the “none of these” option is
an interesting addition to the modelling process, highlighting situations where
no purchase will be made because nothing is attractive enough to be
acceptable. However, the primary consideration is how attractive a product is
and to degree to which people want to buy it.
In this study we are faced with a slightly different context in which to use the
conjoint results. We can still use the values in exactly the same way as the
any other conjoint based approach, but our objectives are slightly different
from those of many other studies.
Here it is interesting to know how attractive a package is, but this simply tells
us what customers want to be offered and which package they want the most.
Our requirement is to identify what customers truly need to be offered rather
than what they want. This is not just about how attractive an offer is, therefore,
but also about how it relates to the threshold set by the “none of these is
acceptable” option.
For this reason we have chosen to focus on acceptability rather than
attractiveness in evaluating potential service offers. We know that many of the
offers tested are less attractive than the current offer (given that a majority of
the packages that are tested represent changes that reduce the level of
service offered). However, it is possible for a package to be less attractive
than the current offer, but still be attractive enough to be considered
Residential customer needs Appendix 6 – Conjoint analysis
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acceptable. In order to focus on what level of service customers need, we
have evaluated service offers in terms of how many customers find them
acceptable or unacceptable (i.e. those who find they do or do not meet their
needs).
What is clear from the data is that the threshold at which the service becomes
unacceptable is, for a majority of respondents, some way below the level of
the current service. This is not to say that customers want a service which is
inferior to the current one, nor that they would be happy to see such a
deterioration in service - they would certainly want to keep as high a level of
service as possible. However, they are prepared to tolerate lower levels of
service, which are still sufficient to be considered an acceptable universal
service, even though they are less attractive that the most desirable offer.
Conjoint output
The choices made in the exercise or preferences for each offer are analysed
to identify what lies behind these choices. The conjoint analysis breaks down
the preferences into the impact that each feature has on that preference.
What we end up with is a set of values (known as utilities) which explain the
choices that customers make.
A utility value is calculated for each level of each feature, and is essentially a
measure of how attractive that level is. Utilities are not measured on a
particular scale, but are relative measures – they indicate how much more or
less attractive one level is compared with another.
From the values of each level, it is also common in conjoint analysis to
calculate the importance of each feature. This is determined by the ability of a
feature to influence customers in their final choices – if there is a bigger
difference in the attractiveness of the levels of a feature, then that feature will
potentially exert a greater influence on the attractiveness of the final service
offer. It will, therefore, be reported as being more important.
Examples of how the conjoint output is reported are shown below. The
importance of features is traditionally presented as a percentage, which may,
therefore, be shown like this, revealing that the choice between the one and
two tier service has had most influence on the overall choices
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Importance of features - DUMMY DATA
10.7
12.2
13.5
18.9
20.0
24.8
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Ability to specify evening /Saturday delivery
Additional delivery options
Collection and deliveryservice
Price
Quality of service: 1st classpost
1 v 2 tier service
Within attributes the values of the levels may be viewed like this. It is
important to remember here that the figures do not represent values on
a specific scale – they are relative measures.
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Relative attractiveness of service levels / price l evels - DUMMY DATA
0
80
120
110
100
60
00
20
40
60
80
100
120
140
75% 85% 93% 41p / 32p 45p / 35p 49p / 38p 55p / 42p
From the chart above we can see the relative value of different levels of
service. As we would expect there is a drop in attractiveness as quality of
service declines or price increases, but what is important about these values
is that we can see how the different levels compare. We can see that dropping
quality of service from 85% to 75% produces twice as big a decline in
attractiveness than dropping from 93% to 85%. Similarly, one price increase
from 41/32p to 45/35p has little impact, but a second increase to 49/38p has a
much more dramatic effect on perceptions of the service.
We can also compare across attributes, and see that dropping the quality of
service to 85% has a bigger negative impact than increasing the price to
45/35p. We could infer from this, therefore, that it would be better to maintain
the level of service and increase the price, than to keep prices to current
levels and allow quality to decline.
This type of trade-off between the levels of the features is what we need to
know to be able to answer key questions on what service should be offered.
While using the summary numbers to indicate what might happen is
interesting, this is where the conjoint model is of much more use.
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Conjoint model
A conjoint model takes the data and uses it to simulate what happens under
different conditions. Rather than simply inferring from the data what the
correct approach would be, we can actually test this and see the impact of
different potential changes on customer perceptions of the service.
The model is an easy to use Excel based tool that allows the user to define a
number of services and identify which is preferred. It also identifies scenarios
where no option which is offered is sufficiently attractive and can also model
those saying that nothing on offer is acceptable.
The utility levels and attribute importance scores provide good summary
measures of what happens in the market, and they offer an excellent way of
contrasting different customer groups but, they are not the best way of making
decisions on the future direction of the service. For this, a better approach is
to construct a model using the conjoint data.
The summary scores generated by the model can hide differences between
respondents – for example we can except that they would all rather have a
service six days a week, but whether or not a Monday to Friday service is
better than retaining Saturday services and dropping a weekday is a matter of
personal opinion. The average scores can disguise this level of difference
between individuals.
In the following screenshot from the demo model, we are testing four possible
strategies – raising prices, dropping the Saturday service, reducing the quality
of service and having a single service. The model shows the percentage that
would choose each option.
Residential customer needs Appendix 6 – Conjoint analysis
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Choice (%)
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The number of offers included in the model can be varied, so it can also be
used to answer simpler questions. In the following example we test just two
offers - a single tier two day service against the current service offer and see
that it is chosen by a lower percentage.
Residential customer needs Appendix 6 – Conjoint analysis
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Choice (%)
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We can then change the current service and see how this impacts on the
choice. If, as shown in the following example the choice was between a much
more expensive two tier service or a single tier two day service, then opinions
start to change and the single tier two day service has a slight advantage.
Residential customer needs Appendix 6 – Conjoint analysis
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Choice (%)
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From this sort of analysis we can see that for many people, the two tier
service is very important and that they are prepared to pay considerably more
to retain it, and yet there are others for whom the idea of a single tier two day
service is quite appealing, even when compared to the current two tier service
and prices.
We can even use the model to test a single tier two day service offer – in the
next example we look at reactions to offering a single tier two day service. We
see that most of those who preferred the two tier approach accept this
(although we know they didn’t want it) but do also see a sizeable increase in
those who view this offer as unacceptable.
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Choice (%)
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The model allows this type of simulation both in total and by subgroup, so the
appeal and acceptability of service offers can be evaluated for key customer
segments.